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Robust Optimization-Based Iterative Learning Control for Nonlinear Systems With Nonrepetitive Uncertainties
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DOI：10.1109/JAS.2021.1003973.png)
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Abstract
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This paper aims to solve the robust iterative learning control (ILC) problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties. A new optimization-based method is proposed to design and analyze adaptive ILC, for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices. It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC, where the boundedness of system trajectories and estimated parameters can be ensured, regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties. Two simulation tests, especially implemented for an injection molding process, demonstrate the effectiveness of our robust optimization-based ILC results.
Keywords:
Adaptive iterative learning control (ILC)
nonlinear time-varying system
robust convergence
substochastic matrix
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